Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe.

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950-2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios.

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe.

Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they're collected from the "Land Use/Cover Area frame Statistical Survey" (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and "Soil Transformations in European Catchments" (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960-1990 and 2000-2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%.

A knowledge-based approach to estimating the magnitude and spatial patterns of potential threats to soil biodiversity.

Because of the increasing pressures exerted on soil, below-ground life is under threat. Knowledge-based rankings of potential threats to different components of soil biodiversity were developed in order to assess the spatial distribution of threats on a European scale. A list of 13 potential threats to soil biodiversity was proposed to experts with different backgrounds in order to assess the potential for three major components of soil biodiversity: soil microorganisms, fauna, and biological functions. This approach allowed us to obtain knowledge-based rankings of threats. These classifications formed the basis for the development of indices through an additive aggregation model that, along with ad-hoc proxies for each pressure, allowed us to preliminarily assess the spatial patterns of potential threats. Intensive exploitation was identified as the highest pressure. In contrast, the use of genetically modified organisms in agriculture was considered as the threat with least potential. The potential impact of climate change showed the highest uncertainty. Fourteen out of the 27 considered countries have more than 40% of their soils with moderate-high to high potential risk for all three components of soil biodiversity. Arable soils are the most exposed to pressures. Soils within the boreal biogeographic region showed the lowest risk potential. The majority of soils at risk are outside the boundaries of protected areas. First maps of risks to three components of soil biodiversity based on the current scientific knowledge were developed. Despite the intrinsic limits of knowledge-based assessments, a remarkable potential risk to soil biodiversity was observed. Guidelines to preliminarily identify and circumscribe soils potentially at risk are provided. This approach may be used in future research to assess threat at both local and global scale and identify areas of possible risk and, subsequently, design appropriate strategies for monitoring and protection of soil biota.

Contaminated sites in Europe: review of the current situation based on data collected through a European network.

Under the European Union (EU) Thematic Strategy for Soil Protection, the European Commission has identified soil contamination as a priority for the collection of policy-relevant soil data at European scale. In order to support EU soil management policies, soil-related indicators need to be developed which requires appropriate data collection and establishment of harmonized datasets for the EU Member States. In 2011-12, the European Soil Data Centre of the European Commission conducted a project to collect data on contaminated sites from national institutions in Europe using the European Environment Information and Observation Network for soil (EIONET-SOIL). This paper presents the results obtained from analysing the soil contaminated sites data submitted by participating countries. According to the received data, the number of estimated potential contaminated sites is more than 2.5 million and the identified contaminated sites around 342 thousand. Municipal and industrial wastes contribute most to soil contamination (38%), followed by the industrial/commercial sector (34%). Mineral oil and heavy metals are the main contaminants contributing around 60% to soil contamination. In terms of budget, the management of contaminated sites is estimated to cost around 6 billion Euros (€) annually.

Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection.

Under the European Union Thematic Strategy for Soil Protection, the European Commission Directorate-General for the Environment and the European Environmental Agency (EEA) identified a decline in soil organic carbon and soil losses by erosion as priorities for the collection of policy relevant soil data at European scale. Moreover, the estimation of soil organic carbon content is of crucial importance for soil protection and for climate change mitigation strategies. Soil organic carbon is one of the attributes of the recently developed LUCAS soil database. The request for data on soil organic carbon and other soil attributes arose from an on-going debate about efforts to establish harmonized datasets for all EU countries with data on soil threats in order to support modeling activities and display variations in these soil conditions across Europe. In 2009, the European Commission's Joint Research Centre conducted the LUCAS soil survey, sampling ca. 20,000 points across 23 EU member states. This article describes the results obtained from analyzing the soil organic carbon data in the LUCAS soil database. The collected data were compared with the modeled European topsoil organic carbon content data developed at the JRC. The best fitted comparison was performed at NUTS2 level and showed underestimation of modeled data in southern Europe and overestimation in the new central eastern member states. There is a good correlation in certain regions for countries such as the United Kingdom, Slovenia, Italy, Ireland, and France. Here we assess the feasibility of producing comparable estimates of the soil organic carbon content at NUTS2 regional level for the European Union (EU27) and draw a comparison with existing modeled data. In addition to the data analysis, we suggest how the modeled data can be improved in future updates with better calibration of the model.

Assessment of the water quality of Troia for the multipurpose usages.

The aim of this study was to determine the origin and quality of waters in Troia. For this purpose total of 25 water samples including 2 springs, 14 surfaces and 9 groundwaters, were collected at eight different times. Global positioning system (GPS) was used to determine to coordinates of sampling points. The concentration of 6 minor elements (B, Cu, F, Fe, Pb and Zn), 9 major anions and cations (Na(+), Ca(2+), K(+), Mg(2+), SO(4)(2-), PO(4)(3-), HCO(3)(-), Cl(-) and, CO(3)(2-)) were determined by spectrometric, colorimetric and volumetric methods. Water pH, EC, DO, ORP and TDS were measured in situ using probes. The data showed that the concentrations of most of minor elements were below the EPA and TSE limits except Pb which ranged between 0.001 and 4.832 mg L(-1). Statistically significant relationships (P<0.01 and r>0.70) were observed between Fe and Cu, Cu and K(+), Cu and Ca(2+), B and Na(+), Na(+) and K(+). Assessing the water based on irrigation using Wilcox model showed that some well waters were not suitable for irrigation. Troia water was found to be highly corrosive and the average corrosion coefficients varied from 0.5 to 4.6. According to the Piper and Schoeller diagrams results, the water in Troia was classified as mixed water type.